Alternative foraging strategies among bears fishing for salmon: a test of the dominance hypothesis
Bibliographic record
Abstract
Previous studies of bears (genus Ursus L., 1758) fishing for Pacific salmon (genus Oncorhynchus Suckley, 1861) suggest that dominant individuals are the most efficient foragers owing to their ability to secure access to the most productive locations. We tested this hypothesis by observing brown bears ( Ursus arctos L., 1758) fishing for chum salmon ( Oncorhynchus keta (Walbaum in Artedi, 1792)) at McNeil River, Alaska. We did not observe strong relationships between the foraging efficiency of individual bears and the frequency with which they engaged in dominance-related behaviors (e.g., displacing competitors, stealing fish, using more popular or productive locations). Although some dominant individuals achieved high catch rates, other nondominant bears foraged with comparable or greater efficiency by developing alternative strategies adapted to specific locations. Our observations demonstrate that bears may employ a variety of fishing strategies, the success of which may be location-specific and frequency-dependent. These findings suggest that physical and cognitive skills may be as important as social dominance in determining foraging success among bears.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".